Adaptive Decision Mechanism Research Articles

MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.

Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of protein information, including sequence, structure and interaction network. Among them, protein sequences have gained significant attention where methods are investigated to extract the information from different views of features. However, how to fully exploit the views for effective protein sequence analysis remains a challenge. In this regard, we propose a multi-view, multi-scale and multi-attention deep neural model (MMSMA) for protein function prediction. First, MMSMA extracts multi-view features from protein sequences, including one-hot encoding features, evolutionary information features, deep semantic features and overlapping property features based on physiochemistry. Second, a specific multi-scale multi-attention deep network model (MSMA) is built for each view to realize the deep feature learning and preliminary classification. In MSMA, both multi-scale local patterns and long-range dependence from protein sequences can be captured. Third, a multi-view adaptive decision mechanism is developed to make a comprehensive decision based on the classification results of all the views. To further improve the prediction performance, an extended version of MMSMA, MMSMAPlus, is proposed to integrate homology-based protein prediction under the framework of multi-view deep neural model. Experimental results show that the MMSMAPlus has promising performance and is significantly superior to the state-of-the-art methods. The source code can be found at https://github.com/wzy-2020/MMSMAPlus.

Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets, which is based on a gene regulatory network, an adaptive decision mechanism, and an improved Vicsek-model. Using the gene regulatory network method, the robots can generate entrapping patterns according to the environmental input, including the positions of the targets and obstacles. Next, an adaptive decision mechanism is proposed, allowing each robot to choose the most well-adapted capture point on the pattern, based on its environment. The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly, without colliding with other robots or obstacles. The proposed decision mechanism, combined with the improved Vicsek-model, can form a uniform entrapment shape and create a revolving effect around targets while entrapping them. This study also enables swarm robots, with an adaptive pattern formation, to entrap multiple targets in complex environments. Swarm robots can be deployed in the military field of unmanned aerial vehicles’ (UAVs) entrapping multiple targets. Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

Faster Detection of Microgrid Islanding Events using an Adaptive Ensemble Classifier

A novel scheme for fast detection of islanding events in a microgrid is proposed in this paper. The scheme consists of a passive islanding detection process using an adaptive ensemble classifier and measurements of three-phase voltage at the distributed generation (DG) terminals. Initial features inputs to the classifier are obtained using the phase-space method. They are then used to train an ensemble classifier which comprises a set of randomized neural networks called extreme learning machine (ELM). The optimal parameter settings for each ELM are identified through evolutionary computation. An adaptive decision mechanism is designed to progressively adjust the decision time of the events classification, where its decision speed can be significantly improved over existing methods. Extensive tests are conducted on a radial distribution system with two identical DG units and the IEEE 33-bus with four DG units, considering various scenarios of islanding and non-islanding conditions, as well as the different types of DG. The simulation results show that the proposed scheme can make accurate decisions at appropriately earlier times, which achieves a good balance between efficiency and accuracy in detecting the islanding events.

複数ブロックエージェントの自律行動の獲得に関する研究

This paper describes the autonomous behavior of block shaped agents existing in a spatially constrained environment. The difficulty of this problem is the determination of the behavior of a block agent under a complex environment dynamically produced mutual actions of many block agents. Moving obstacle avoidance problem causes this difficulty resulting from the autonomous motion of block agents. The objective of this research is to build an adaptive decision mechanism of the behavior for the autonomous block agents when they have their own tasks. Specifically, this paper shows one case study of the proposed mechanism for the problem of removing blocks from a container. It is assumed that the agents are block shaped, and the positions and dimensions of each block are different. Our approach adopted is the Classifier System based architecture. The results of simulation experiments are presented which indicate the possibility of this architecture in allowing block agents to adapt to dynamic environments.

Genetically based differences in foraging behaviour among sympatric morphs of arctic charr (Pisces: Salmonidae)

Abstract. Four morphs of arctic charr, Salvelinus alpinus, coexist in the landlocked lake Thingvallavatn, Iceland. The morphs occupy different habitats and are segregated in diet year round. Feeding trials, involving laboratory-reared progeny of three morphs, indicated that several behavioural differences are genetically based, supporting the claim that the segregation among the morphs in Thingvallavatn is a consequence of adaptive trophic specializations. When Daphnia magna were fed, one at a time, progeny of small benthivorous charr noticed the prey earlier, and were more active, than progeny of planktivorous and piscivorous charr. Although prey rejection was more frequent among small benthivorous charr their feeding rate at high prey density equalled that of planktivorous chart. At lower prey densities small benthivorous charr, although showing reduced feeding rates, maintained higher feeding rates than the planktivorous fish. In the wild, planktivorous charr feed in the open pelagic, so this counterintuitive finding may indicate an innate 'predation-risk versus energy-gain trade-off'. In the experiments the reluctance of the planktivorous fish to feed at low prey densities may reflect an adaptive decision mechanism geared to maximize energy returns.

A Comparison of Optimal and Adaptive Decision Mechanisms in an Organizational Setting

This paper uses simulation experiments to compare optimal and adaptive decision mechanisms for firms modelled as operating in a duopoly. Optimal decision policies are derived using a dynamic programming approach based on the Cyert-DeGroot (Cyert, R. M., M. H. DeGroot. 1970. Muhiperiod decision models with alternating choice as a solution to the duopoly problem. Quart J. Econom. 84 410–429.) duopoly models, while the adaptive decision mechanism uses the Cyert-March behavioral theory of the firm (Cyert, Richard, James March. 1963. A Behavioral Theory of the Firm. Prentice-Hall, Englewood Cliffs, New Jersey.). Using a cetera paribus approach that unequivocally establishes causality, the simulations develop conditions under which adaptive decision processes generate profit performance comparable to that generated by normative models. The simulations suggest that good organizational performance requires both good decision making processes and efficient operation of the organization, that good performance can be achieved in at least three ways: by employing optimal decision processes; by the use of adaptive processes in a highly competitive environment; or by the use of adaptive processes when management has high aspirations. The implications of these findings for implementing management science studies of organization decision-making are considered.